Synthesis Lectures on Technology Management & Entrepreneurship – serie

Financial statements and information drawn from them confront us daily: in the media, in corporate annual reports, in the treasurer’s reports for clubs or religious groups, in documents provided to employees and managers, as one considers alternative investments, in documents provided by homeowners’ association and government agenciesVarious readers of a company’s “financial score” make decisions based on financial information: the company’s managers devise actions to improve operations; investors buy or sell the corporation’s securities; creditors decide how much to lend; customers judge the reliability of this supplier; potential employees decide whether to invest their careers in the company.If you are training to be an accountant, find another book. This book’s objective is to increase your ability to draw useful information from financial statements, and thus to make better decisions—in both your personal life and your professional life. Studying this book should help you be a better manager. That is both its objective and its perspective.The book starts at square one; it assumes no prior knowledge on your part. To increase your financial literacy, you will learn the common nomenclature (but not esoteric jargon) used by accountants and financial experts. You will be equipped to ask insightful questions of experts, to engage them and your colleagues in thoughtful debates about financial and accounting issues, and to make better decisions.Table of Contents: The Balance Sheet / The Income Statement / Valuation / Timing / Capital Structure / Cash Flow / Evaluating with Ratios / Cost Accounting / Budgeting and Forecasting / Rules and Integrity / Appendix: Scorekeeping at Not-for-Profits

Success in careers outside the university setting depends on an individual's capacity to master professional skills and respond appropriately to dynamic situations with flexibility, adaptation, and innovative thinking. This book describes a simple, common sense method of how to include professional skills training in any curricula without compromising academic rigor. It relies on introduction of unanticipated yet manageable crises simulating scenarios commonly experienced in the workplace. The method promises to inspire both students and their teachers to conquer new territory outside their comfort zones. Examples include how to respond to a demand for innovation and teamwork, a lay-off, a re-organization, or switching jobs and projects. These situations are bound to occur for most people and in most jobs they often create stress and, perhaps, despair. Preparing and practicing a mindful and healthy response is beneficial, and now this process can be performed in the classroom, while it serves as a platform for character building prior to unexpected real-life events.Key Features:- Description of the importance of, incentives for, and rewards of exiting the comfort zone- Principles for teaching and learning professional skills- Student anecdotes and reflection- Rubric entries and assessment of learningTable of Contents: The Comfort Zone and “Being out of It” / Exiting the Comfort Zone: Reasons and Impact / Getting Educators and Students out of the Comfort Zone / Principles of “Out-of-the-Comfort-Zone” (OOC) Teaching / Anecdotes of OOC Learning / Measuring the Outcome

Many of us wish we could design inventions and make decisions that were optimal and sustainable, but we do not know how to begin the approach. This book offers a guide to dramatically improve the quality of innovation and solution-making through the respectful use of existing and abundant, but often-ignored, resources. Sustainable innovation is about creative combination of ideas, materials, methods, and people, courage to derive value from opposition and diversity, integrative intelligence, virtuous planning, minimal consumption of resources, and definition of alternative plans. Using the method successfully requires that we are truly interested in the common good of humankind, that we care about our environment, and that we take time to think carefully about consequences before we act, invent, or make decisions. It is a call for a much-needed collaboration between people of different backgrounds, skills, and opinions with the intent to preserve individual and local integrity and adopt a win-win mindset. Try it, and partake in its positive and long-lasting effects!Key Features- Description of sustainable innovation and the untapped innovation potential- Sustainable innovation requirements- Sustainable innovation attitudes- Step-by-step guide to sustainable innovation- Application to 21st century challenges- A global win-win scenarioTable of Contents: Innovation Attitudes / Inspiration from the Natural and Behavioral Sciences / Untapped Sustainable Innovation Potential / Step-by-Step Guide to Sustainable Innovation / Notes on the Application to 21st century challenges/ Personal Step-by-Step Work Section / Looking to the Future

This book aims to increase the success rates of startups by focusing on value proposition innovation, which is propelled by the involvement of potential consumers as well as other resources such as freelancers and strategic relationships with academia. The author shows how startups who are resource constrained can invest efforts exploring the potential market of their products. The author also explores how global markets can be beneficial for a startup’s success, while showing the workarounds in hard-to-access markets. The book investigates gaining knowledge shared by freelancers, customers, and academia, whose involvement can be crucial in supporting value proposition innovation activities such as ideas generation, implementation, and commercialization. Combined, the author leads readers to discover their ability to foster value proposition innovations that result into long term competitive advantage in a highly fluctuating business environment.

This book aims to increase the success rates of startups by focusing on value proposition innovation, which is propelled by the involvement of potential consumers as well as other resources such as freelancers and strategic relationships with academia. The author shows how startups who are resource constrained can invest efforts exploring the potential market of their products. The author also explores how global markets can be beneficial for a startup’s success, while showing the workarounds in hard-to-access markets. The book investigates gaining knowledge shared by freelancers, customers, and academia, whose involvement can be crucial in supporting value proposition innovation activities such as ideas generation, implementation, and commercialization. Combined, the author leads readers to discover their ability to foster value proposition innovations that result into long term competitive advantage in a highly fluctuating business environment.

This book develops a quantitative stock market investment methodology using financial indicators that beats the benchmark of S&P500 index. To achieve this goal, an ensemble of machine learning models is meticulously constructed, incorporating four distinct algorithms: support vector machine, k-nearest neighbors, random forest, and logistic regression. These models all make use of financial ratios extracted from company financial statements for the purposes of predictive forecasting. The ensemble classifier is subject to a strict testing of precision which compares it to the performance of its constituent models separately. Rolling window and cross-validation tests are used in this evaluation in order to provide a comprehensive assessment framework. A risk-off filter is developed to limit risk during uncertain market periods, and consequently to improve the Sharpe ratio of the model. The risk adjusted performance of the final model, supported by the risk-off filter, achieves a Sharpe ratio of 1.63 which surpasses both the model’s performance without the filter that delivers Sharpe ratio of 1.41 and the one from the S&P500 index of 0.80. The substantial increase in risk-adjusted returns is accomplished by reducing the model’s volatility from an annual standard of deviation of 15.75% to 11.22%, which represents an almost 30% decrease in volatility.

This book develops a quantitative stock market investment methodology using financial indicators that beats the benchmark of S&P500 index. To achieve this goal, an ensemble of machine learning models is meticulously constructed, incorporating four distinct algorithms: support vector machine, k-nearest neighbors, random forest, and logistic regression. These models all make use of financial ratios extracted from company financial statements for the purposes of predictive forecasting. The ensemble classifier is subject to a strict testing of precision which compares it to the performance of its constituent models separately. Rolling window and cross-validation tests are used in this evaluation in order to provide a comprehensive assessment framework. A risk-off filter is developed to limit risk during uncertain market periods, and consequently to improve the Sharpe ratio of the model. The risk adjusted performance of the final model, supported by the risk-off filter, achieves a Sharpe ratio of 1.63 which surpasses both the model’s performance without the filter that delivers Sharpe ratio of 1.41 and the one from the S&P500 index of 0.80. The substantial increase in risk-adjusted returns is accomplished by reducing the model’s volatility from an annual standard of deviation of 15.75% to 11.22%, which represents an almost 30% decrease in volatility.

This is the most up-to-date book on knowledge management. It covers the principles of knowledge management and translates them to actionable strategies and concrete suggestions. It highlights a novel knowledge management approach that is based on collaboration and inclusiveness that aims to address present risks and future challenges at the societal level. The book is written in a way that makes this important topic accessible to a broad readership.

This is the most up-to-date book on knowledge management. It covers the principles of knowledge management and translates them to actionable strategies and concrete suggestions. It highlights a novel knowledge management approach that is based on collaboration and inclusiveness that aims to address present risks and future challenges at the societal level. The book is written in a way that makes this important topic accessible to a broad readership.

This book presents the first practice-based account of Generative AI (GenAI) integration in postgraduate courses, structured across three approaches: course redesign and delivery, assessment redesign, and evaluation adaptations. The book introduces GenAI as a knowledge collaborator and reports the practical approaches, such as redesigning group activities into applied, research-driven tasks, providing detailed GenAI usage instructions, and requiring acknowledgement and reporting tools such as AI Contribution Forms, GenAI Integration Rubrics, and AI Contribution Assessment Forms, while adapting evaluation rubrics to ensure that the technology cannot act as a substitute for student work. Through guided experimentation in these areas, students were able to explore GenAI’s capabilities and limitations, reflect on appropriate and inappropriate use cases, and strengthen their digital literacy, reflective synthesis, and ethical awareness.A rigorous Monitoring & Evaluation (M&E) framework, built on a Theory of Change, pretested survey instruments, and detailed research protocols, was used to assess impacts on student knowledge, confidence, application, and awareness, both in the short term and the medium term. All instruments and procedures are included, making them directly reusable or adaptable by peers seeking to replicate or scale similar interventions in their courses.This book is a practical and methodological resource for educators and institutions, offering tested integration strategies, ready-to-use instruments, and a replicable evaluation framework to embed GenAI responsibly and effectively into academic practice while safeguarding academic integrity.

Large Language Models (LLMs) have disruptively changed the world of AI for good and their adoption is near universal. However, how many know that they have a big limitation while processing large numerical quantitative business datasets usually found in ERPs as 1000s of tables. LLMs cannot process 100s of spreadsheets or tables at one time and when they try, they either fail to run or generate inaccurate predictions at best.The authors of this book propose LNMs or Large Numerical Models as a parallel universe to LLMs. LNMs are designed and built for numerical datasets and they offer some significant advantages over LLMs such as very accurate predictions, no hallucinations, improvement in business outcomes and ability to deliver in a "cold start" environment. LNMs are vertically curated and can run on a CPU as opposed to energy guzzling GPUs or water consuming cooling systems that LLMs need.This book introduces LNMs, its underlying structure and SXI. SXI is to LNM as GPT is to LLMs, the underlying core science and technology. The authors also present specific applications of LNMs in healthcare, fintech, wireless, supplychain, marketing campaigns. Finally, the authors introduce their current research area of LLNMs. LLNM combines both LLM and LNM and has significant potential advantages over either LLM or LNMs.